GeoPandas

Geometries for Vector Spatial Data

import matplotlib.pyplot as plt
from matplotlib.patches import Polygon as MplPolygon

# Data for streets (line strings)
streets = [
    [(0, 0), (1, 2), (3, 3)],
    [(3, 3), (5, 2), (7, 5)],
    [(7, 5), (8, 8), (10, 10)],
    [(1, 2), (2, 5), (4, 6)],
]

# Data for school catchments (polygons)
catchments = [
    [(0.5, 0.5), (2, 1), (1.5, 3), (0.5, 2)],
    [(3, 4), (5, 3.5), (6, 6), (4, 6.5)],
    [(7, 7), (8.5, 7.5), (9, 9), (7.5, 9)],
]

# Adjusting the data for one school per catchment
schools = [(1.2, 1.8), (4.7, 5.3), (8.2, 8.2)]

# Plotting the GIS map with one school per catchment
fig, ax = plt.subplots(figsize=(8, 8))

# Plotting the streets with one legend entry
ax.plot(*zip(*streets[0]), color='blue', linewidth=2, label="Streets")
for street in streets[1:]:
    street_x, street_y = zip(*street)
    ax.plot(street_x, street_y, color='blue', linewidth=2)

# Plotting the catchments with legend
for i, catchment in enumerate(catchments):
    polygon = MplPolygon(catchment, closed=True, color='orange', alpha=0.5, edgecolor='black')
    ax.add_patch(polygon)
    if i == 0:
        polygon.set_label("School Catchments")

# Plotting the schools (points) with one per catchment
school_x, school_y = zip(*schools)
ax.scatter(school_x, school_y, color='green', s=100, label="Schools")

# Set the legend and title
ax.legend()
ax.set_title('Vector GIS: Street Network, School Catchments, and Schools')
ax.set_xlim(-1, 11)
ax.set_ylim(-1, 11)
ax.set_aspect('equal')

# Save the figure to a file
plt.savefig("vector.png", dpi=300)

# Show the plot
plt.show()

/tmp/ipykernel_2058626/3972444099.py:33: UserWarning: Setting the 'color' property will override the edgecolor or facecolor properties.
  polygon = MplPolygon(catchment, closed=True, color='orange', alpha=0.5, edgecolor='black')

Here, we are faking it regarding a GIS, as matplotlib is a visualization library and doesn’t actually allow us to do any spatial analysis per se.

from shapely import Point

school_points = [Point(school) for school in schools]

school_points

[<POINT (1.2 1.8)>, <POINT (4.7 5.3)>, <POINT (8.2 8.2)>]

school_points[0]

school_0, school_1, school_2 = school_points

school_0

school_1

school_2

from shapely import Polygon

catchment_polygons = [Polygon(catchment) for catchment in catchments]

catchment_0, catchment_1, catchment_2 = catchment_polygons

catchment_0

catchment_1

catchment_2

from shapely import LineString

street_lines = [LineString(street) for street in streets]

street_0, street_1, street_2, street_3 = street_lines

street_0

street_1

street_2

street_3

Geometry Types

school_0.geom_type

'Point'

school_2.geom_type

'Point'

school_0.area

0.0

school_0.length

0.0

list(school_0.coords)

[(1.2, 1.8)]

list(school_2.coords)

[(8.2, 8.2)]

school_0.distance(school_2)

9.4847245611035

street_1.geom_type

'LineString'

street_1.area

0.0

street_1.length

5.841619252963779

school_0.distance(street_1)

2.1633307652783933

catchment_0.geom_type

'Polygon'

catchment_0.area

2.375

list(catchment_0.exterior.coords)

[(0.5, 0.5), (2.0, 1.0), (1.5, 3.0), (0.5, 2.0), (0.5, 0.5)]

catchment_0.bounds

(0.5, 0.5, 2.0, 3.0)

school_0.distance(catchment_0)

0.0

catchment_0.contains(school_0)

True

catchment_0.contains(school_1)

False

Geopandas

import geopandas as gpd

streets = gpd.GeoSeries(street_lines)

streets.plot()

catchments = gpd.GeoSeries(catchment_polygons)

catchments.plot()

schools = gpd.GeoSeries(school_points)

schools.plot()

type(schools)

geopandas.geoseries.GeoSeries

dir(schools)

['T',
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 'idxmax',
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schools_gdf = gpd.GeoDataFrame(geometry=schools)

schools_gdf.head()

	geometry
0	POINT (1.2 1.8)
1	POINT (4.7 5.3)
2	POINT (8.2 8.2)

type(schools_gdf)

geopandas.geodataframe.GeoDataFrame

schools_gdf['students'] = [124, 94, 100]

schools_gdf.head()

	geometry	students
0	POINT (1.2 1.8)	124
1	POINT (4.7 5.3)	94
2	POINT (8.2 8.2)	100

schools_gdf.plot()

schools_gdf.plot(column='students')

streets_gdf = gpd.GeoDataFrame(geometry=streets)

streets_gdf.plot()

streets_gdf['length'] = streets_gdf.length

streets_gdf.head()

	geometry	length
0	LINESTRING (0 0, 1 2, 3 3)	4.472136
1	LINESTRING (3 3, 5 2, 7 5)	5.841619
2	LINESTRING (7 5, 8 8, 10 10)	5.990705
3	LINESTRING (1 2, 2 5, 4 6)	5.398346

streets_gdf.plot(column='length', legend=True)

catchments_gdf = gpd.GeoDataFrame(geometry=catchment_polygons)

catchments_gdf['area'] = catchments_gdf.area
catchments_gdf.plot(column='area', legend=True)

Southern California Census Tracts

gdf = gpd.read_parquet("~/data/scag_region.parquet")

gdf.shape

(4580, 194)

type(gdf)

geopandas.geodataframe.GeoDataFrame

gdf.head()

	geoid	n_asian_under_15	n_black_under_15	n_hispanic_under_15	n_native_under_15	n_white_under_15	n_persons_under_18	n_asian_over_60	n_black_over_60	n_hispanic_over_60	...	year	n_total_housing_units_sample	p_nonhisp_white_persons	p_white_over_60	p_black_over_60	p_hispanic_over_60	p_native_over_60	p_asian_over_60	p_disabled	geometry
0	06037128702	58.0	0.0	223.0	0.0	475.0	986.0	NaN	NaN	NaN	...	2010	2903.0	64.726214	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.4487 34.16485, -118.43997 34.16...
1	06037131600	83.0	62.0	777.0	0.0	135.0	1355.0	NaN	NaN	NaN	...	2010	1487.0	28.679979	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.56229 34.22033, -118.55792 34.2...
2	06037134104	287.0	17.0	816.0	0.0	61.0	1323.0	NaN	NaN	NaN	...	2010	1388.0	14.846188	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.57976 34.21558, -118.57539 34.2...
3	06037134304	90.0	24.0	298.0	0.0	89.0	520.0	NaN	NaN	NaN	...	2010	928.0	33.378933	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.61472 34.21952, -118.61039 34.2...
4	06037242000	0.0	229.0	681.0	0.0	0.0	1164.0	NaN	NaN	NaN	...	2010	1054.0	0.058565	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.25416 33.93882, -118.25413 33.9...

5 rows × 194 columns

gdf.columns.values

array(['geoid', 'n_asian_under_15', 'n_black_under_15',
       'n_hispanic_under_15', 'n_native_under_15', 'n_white_under_15',
       'n_persons_under_18', 'n_asian_over_60', 'n_black_over_60',
       'n_hispanic_over_60', 'n_native_over_60', 'n_persons_over_60',
       'n_white_over_60', 'n_asian_over_65', 'n_black_over_65',
       'n_hispanic_over_65', 'n_native_over_65', 'n_white_over_65',
       'n_persons_over_75', 'n_persons_over_15', 'n_civilians_over_16',
       'n_civilians_over_18', 'n_persons_over_25', 'n_age_5_older',
       'n_asian_age_distribution', 'n_black_age_distribution',
       'n_hispanic_age_distribution', 'n_native_age_distribution',
       'n_white_age_distribution', 'n_asian_persons', 'n_black_persons',
       'n_chinese_persons', 'n_labor_force', 'n_civilians_16_64',
       'n_edu_college_greater', 'n_cuban_pop',
       'n_poverty_determined_asian', 'n_poverty_determined_black',
       'n_total_pop_sample', 'n_female_over_16',
       'n_poverty_determined_families', 'n_poverty_determined_hispanic',
       'n_disabled', 'n_housing_units_multiunit_structures_denom',
       'n_poverty_determined_native', 'n_poverty_determined_persons',
       'n_poverty_determined_white', 'n_employed_over_16',
       'n_total_families', 'n_foreign_born_pop',
       'n_female_headed_families', 'n_filipino_persons',
       'n_female_labor_force', 'n_german_pop', 'n_german_born_pop',
       'n_household_recent_move', 'n_structures_30_old',
       'n_hawaiian_persons', 'n_total_households', 'n_asian_households',
       'n_black_households', 'n_hispanic_households',
       'n_white_households', 'median_household_income',
       'median_income_asianhh', 'median_income_blackhh',
       'median_income_hispanichh', 'median_income_whitehh',
       'n_hispanic_persons', 'n_edu_hs_less', 'n_total_housing_units',
       'per_capita_income', 'n_asian_indian_persons', 'n_irish_pop',
       'n_irish_born_pop', 'n_italian_pop', 'n_italian_born_pop',
       'n_japanese_persons', 'n_korean_persons', 'n_limited_english',
       'n_employed_manufacturing', 'n_married', 'n_mexican_pop',
       'median_home_value', 'median_contract_rent',
       'n_housing_units_multiunit_structures', 'n_recent_immigrant_pop',
       'n_poverty_over_65', 'n_poverty_asian', 'n_naturalized_pop',
       'n_poverty_black', 'n_poverty_families_children',
       'n_nonhisp_black_persons', 'n_poverty_hispanic',
       'n_nonhisp_white_persons', 'n_poverty_native', 'n_poverty_persons',
       'n_native_persons', 'n_poverty_white', 'n_occupied_housing_units',
       'n_other_language', 'n_owner_occupied_housing_units',
       'p_recent_immigrant_pop', 'p_household_recent_move',
       'p_asian_under_15', 'p_black_under_15', 'p_hispanic_under_15',
       'p_native_under_15', 'p_white_under_15', 'p_persons_under_18',
       'p_structures_30_old', 'p_persons_over_60', 'p_asian_over_65',
       'p_black_over_65', 'p_hispanic_over_65', 'p_native_over_65',
       'p_poverty_rate_over_65', 'p_white_over_65', 'p_persons_over_75',
       'p_poverty_rate_asian', 'p_asian_persons', 'p_poverty_rate_black',
       'p_chinese_persons', 'p_edu_college_greater', 'p_cuban_pop',
       'p_foreign_born_pop', 'p_female_headed_families',
       'p_filipino_persons', 'p_female_labor_force',
       'p_poverty_rate_children', 'p_german_pop', 'p_german_born_pop',
       'p_hawaiian_persons', 'p_hispanic_persons',
       'p_poverty_rate_hispanic', 'p_edu_hs_less',
       'p_asian_indian_persons', 'p_irish_pop', 'p_irish_born_pop',
       'p_italian_pop', 'p_italian_born_pop', 'p_japanese_persons',
       'p_korean_persons', 'p_limited_english',
       'p_employed_manufacturing', 'p_married', 'p_mexican_pop',
       'p_housing_units_multiunit_structures', 'p_poverty_rate_native',
       'p_naturalized_pop', 'p_nonhisp_black_persons', 'p_black_persons',
       'p_native_persons', 'p_other_language', 'n_total_pop',
       'p_owner_occupied_units', 'p_poverty_rate', 'p_puerto_rican_pop',
       'p_employed_professional', 'n_puerto_rican_pop',
       'n_employed_professional', 'p_russian_pop', 'p_russian_born_pop',
       'p_scandanavian_pop', 'p_scandanavian_born_pop',
       'p_employed_self_employed', 'p_unemployment_rate',
       'p_vacant_housing_units', 'p_veterans', 'p_vietnamese_persons',
       'p_widowed_divorced', 'p_poverty_rate_white',
       'n_renter_occupied_housing_units', 'n_russian_pop',
       'n_russian_born_pop', 'n_scandaniavian_pop',
       'n_scandaniavian__born_pop', 'n_employed_self_employed',
       'n_unemployed_persons', 'n_vacant_housing_units', 'n_veterans',
       'n_vietnamese_persons', 'n_widowed_divorced', 'n_white_persons',
       'year', 'n_total_housing_units_sample', 'p_nonhisp_white_persons',
       'p_white_over_60', 'p_black_over_60', 'p_hispanic_over_60',
       'p_native_over_60', 'p_asian_over_60', 'p_disabled', 'geometry'],
      dtype=object)

gdf.n_total_pop

0       5497.0
1       5659.0
2       4486.0
3       2924.0
4       3415.0
         ...  
4575    3672.0
4576    5257.0
4577    6765.0
4578    2981.0
4579    3994.0
Name: n_total_pop, Length: 4580, dtype: float64

gdf.geometry

0       POLYGON ((-118.4487 34.16485, -118.43997 34.16...
1       POLYGON ((-118.56229 34.22033, -118.55792 34.2...
2       POLYGON ((-118.57976 34.21558, -118.57539 34.2...
3       POLYGON ((-118.61472 34.21952, -118.61039 34.2...
4       POLYGON ((-118.25416 33.93882, -118.25413 33.9...
                              ...                        
4575    POLYGON ((-118.50373 34.42608, -118.5005 34.42...
4576    POLYGON ((-118.20731 33.90754, -118.20641 33.9...
4577    POLYGON ((-119.22134 34.1813, -119.21727 34.18...
4578    POLYGON ((-116.51068 33.80502, -116.51069 33.8...
4579    POLYGON ((-118.41378 34.1794, -118.4116 34.179...
Name: geometry, Length: 4580, dtype: geometry

gdf.plot()

Projections

gdf.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

gdf1 = gdf.to_crs(3857)

gdf1.crs

<Projected CRS: EPSG:3857>
Name: WGS 84 / Pseudo-Mercator
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: World between 85.06°S and 85.06°N.
- bounds: (-180.0, -85.06, 180.0, 85.06)
Coordinate Operation:
- name: Popular Visualisation Pseudo-Mercator
- method: Popular Visualisation Pseudo Mercator
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

gdf1.plot()

gdf.median_home_value

0       647272.659176
1       400842.977528
2       416741.666667
3       406178.838951
4       251438.857678
            ...      
4575    291838.951311
4576    273871.254682
4577    293254.588015
4578    255794.662921
4579    581717.790262
Name: median_home_value, Length: 4580, dtype: float64

gdf.plot(column='median_home_value')

gdf.plot(column='median_home_value', legend=True)

gdf.plot(column='median_home_value', legend=True,
        scheme='quantiles', k=10)

gdf.plot(column='p_hispanic_persons', legend=True,
        scheme='quantiles', k=10)

gdf.explore(column='p_hispanic_persons', tooltip=['geoid', 'p_hispanic_persons'])

Make this Notebook Trusted to load map: File -> Trust Notebook

gdf.head()

	geoid	n_asian_under_15	n_black_under_15	n_hispanic_under_15	n_native_under_15	n_white_under_15	n_persons_under_18	n_asian_over_60	n_black_over_60	n_hispanic_over_60	...	year	n_total_housing_units_sample	p_nonhisp_white_persons	p_white_over_60	p_black_over_60	p_hispanic_over_60	p_native_over_60	p_asian_over_60	p_disabled	geometry
0	06037128702	58.0	0.0	223.0	0.0	475.0	986.0	NaN	NaN	NaN	...	2010	2903.0	64.726214	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.4487 34.16485, -118.43997 34.16...
1	06037131600	83.0	62.0	777.0	0.0	135.0	1355.0	NaN	NaN	NaN	...	2010	1487.0	28.679979	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.56229 34.22033, -118.55792 34.2...
2	06037134104	287.0	17.0	816.0	0.0	61.0	1323.0	NaN	NaN	NaN	...	2010	1388.0	14.846188	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.57976 34.21558, -118.57539 34.2...
3	06037134304	90.0	24.0	298.0	0.0	89.0	520.0	NaN	NaN	NaN	...	2010	928.0	33.378933	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.61472 34.21952, -118.61039 34.2...
4	06037242000	0.0	229.0	681.0	0.0	0.0	1164.0	NaN	NaN	NaN	...	2010	1054.0	0.058565	NaN	NaN	NaN	NaN	NaN	NaN	POLYGON ((-118.25416 33.93882, -118.25413 33.9...

5 rows × 194 columns

county = gdf.geoid.str[:5]

county

0       06037
1       06037
2       06037
3       06037
4       06037
        ...  
4575    06037
4576    06037
4577    06111
4578    06065
4579    06037
Name: geoid, Length: 4580, dtype: object

gdf['county'] = county

Which county has the most tracts?

gdf.groupby(by='county').count()

	geoid	n_asian_under_15	n_black_under_15	n_hispanic_under_15	n_native_under_15	n_white_under_15	n_persons_under_18	n_asian_over_60	n_black_over_60	n_hispanic_over_60	...	year	n_total_housing_units_sample	p_nonhisp_white_persons	p_white_over_60	p_black_over_60	p_hispanic_over_60	p_native_over_60	p_asian_over_60	p_disabled	geometry
county
06025	31	31	31	31	31	31	31	0	0	0	...	31	31	31	0	0	0	0	0	0	31
06037	2344	2344	2344	2344	2344	2344	2344	0	0	0	...	2344	2344	2328	0	0	0	0	0	0	2344
06059	582	582	582	582	582	582	582	0	0	0	...	582	582	582	0	0	0	0	0	0	582
06065	453	453	453	453	453	453	453	0	0	0	...	453	453	452	0	0	0	0	0	0	453
06071	369	369	369	369	369	369	369	0	0	0	...	369	369	368	0	0	0	0	0	0	369
06073	627	627	627	627	627	627	627	0	0	0	...	627	627	627	0	0	0	0	0	0	627
06111	174	174	174	174	174	174	174	0	0	0	...	174	174	173	0	0	0	0	0	0	174

7 rows × 194 columns

Which county has the largest tract in area?

gdf['area'] = gdf1.area

gdf[['area', 'county']].sort_values(by='area', ascending=False)

	area	county
489	2.700979e+10	06071
437	1.430813e+10	06065
908	1.073840e+10	06071
4303	8.553053e+09	06025
1661	5.345018e+09	06071
...	...	...
715	1.237570e+05	06037
3562	1.161974e+05	06037
4062	9.984444e+04	06037
1669	4.045569e+03	06037
3673	2.537546e-01	06111

4580 rows × 2 columns

gdf.columns.values

array(['geoid', 'n_asian_under_15', 'n_black_under_15',
       'n_hispanic_under_15', 'n_native_under_15', 'n_white_under_15',
       'n_persons_under_18', 'n_asian_over_60', 'n_black_over_60',
       'n_hispanic_over_60', 'n_native_over_60', 'n_persons_over_60',
       'n_white_over_60', 'n_asian_over_65', 'n_black_over_65',
       'n_hispanic_over_65', 'n_native_over_65', 'n_white_over_65',
       'n_persons_over_75', 'n_persons_over_15', 'n_civilians_over_16',
       'n_civilians_over_18', 'n_persons_over_25', 'n_age_5_older',
       'n_asian_age_distribution', 'n_black_age_distribution',
       'n_hispanic_age_distribution', 'n_native_age_distribution',
       'n_white_age_distribution', 'n_asian_persons', 'n_black_persons',
       'n_chinese_persons', 'n_labor_force', 'n_civilians_16_64',
       'n_edu_college_greater', 'n_cuban_pop',
       'n_poverty_determined_asian', 'n_poverty_determined_black',
       'n_total_pop_sample', 'n_female_over_16',
       'n_poverty_determined_families', 'n_poverty_determined_hispanic',
       'n_disabled', 'n_housing_units_multiunit_structures_denom',
       'n_poverty_determined_native', 'n_poverty_determined_persons',
       'n_poverty_determined_white', 'n_employed_over_16',
       'n_total_families', 'n_foreign_born_pop',
       'n_female_headed_families', 'n_filipino_persons',
       'n_female_labor_force', 'n_german_pop', 'n_german_born_pop',
       'n_household_recent_move', 'n_structures_30_old',
       'n_hawaiian_persons', 'n_total_households', 'n_asian_households',
       'n_black_households', 'n_hispanic_households',
       'n_white_households', 'median_household_income',
       'median_income_asianhh', 'median_income_blackhh',
       'median_income_hispanichh', 'median_income_whitehh',
       'n_hispanic_persons', 'n_edu_hs_less', 'n_total_housing_units',
       'per_capita_income', 'n_asian_indian_persons', 'n_irish_pop',
       'n_irish_born_pop', 'n_italian_pop', 'n_italian_born_pop',
       'n_japanese_persons', 'n_korean_persons', 'n_limited_english',
       'n_employed_manufacturing', 'n_married', 'n_mexican_pop',
       'median_home_value', 'median_contract_rent',
       'n_housing_units_multiunit_structures', 'n_recent_immigrant_pop',
       'n_poverty_over_65', 'n_poverty_asian', 'n_naturalized_pop',
       'n_poverty_black', 'n_poverty_families_children',
       'n_nonhisp_black_persons', 'n_poverty_hispanic',
       'n_nonhisp_white_persons', 'n_poverty_native', 'n_poverty_persons',
       'n_native_persons', 'n_poverty_white', 'n_occupied_housing_units',
       'n_other_language', 'n_owner_occupied_housing_units',
       'p_recent_immigrant_pop', 'p_household_recent_move',
       'p_asian_under_15', 'p_black_under_15', 'p_hispanic_under_15',
       'p_native_under_15', 'p_white_under_15', 'p_persons_under_18',
       'p_structures_30_old', 'p_persons_over_60', 'p_asian_over_65',
       'p_black_over_65', 'p_hispanic_over_65', 'p_native_over_65',
       'p_poverty_rate_over_65', 'p_white_over_65', 'p_persons_over_75',
       'p_poverty_rate_asian', 'p_asian_persons', 'p_poverty_rate_black',
       'p_chinese_persons', 'p_edu_college_greater', 'p_cuban_pop',
       'p_foreign_born_pop', 'p_female_headed_families',
       'p_filipino_persons', 'p_female_labor_force',
       'p_poverty_rate_children', 'p_german_pop', 'p_german_born_pop',
       'p_hawaiian_persons', 'p_hispanic_persons',
       'p_poverty_rate_hispanic', 'p_edu_hs_less',
       'p_asian_indian_persons', 'p_irish_pop', 'p_irish_born_pop',
       'p_italian_pop', 'p_italian_born_pop', 'p_japanese_persons',
       'p_korean_persons', 'p_limited_english',
       'p_employed_manufacturing', 'p_married', 'p_mexican_pop',
       'p_housing_units_multiunit_structures', 'p_poverty_rate_native',
       'p_naturalized_pop', 'p_nonhisp_black_persons', 'p_black_persons',
       'p_native_persons', 'p_other_language', 'n_total_pop',
       'p_owner_occupied_units', 'p_poverty_rate', 'p_puerto_rican_pop',
       'p_employed_professional', 'n_puerto_rican_pop',
       'n_employed_professional', 'p_russian_pop', 'p_russian_born_pop',
       'p_scandanavian_pop', 'p_scandanavian_born_pop',
       'p_employed_self_employed', 'p_unemployment_rate',
       'p_vacant_housing_units', 'p_veterans', 'p_vietnamese_persons',
       'p_widowed_divorced', 'p_poverty_rate_white',
       'n_renter_occupied_housing_units', 'n_russian_pop',
       'n_russian_born_pop', 'n_scandaniavian_pop',
       'n_scandaniavian__born_pop', 'n_employed_self_employed',
       'n_unemployed_persons', 'n_vacant_housing_units', 'n_veterans',
       'n_vietnamese_persons', 'n_widowed_divorced', 'n_white_persons',
       'year', 'n_total_housing_units_sample', 'p_nonhisp_white_persons',
       'p_white_over_60', 'p_black_over_60', 'p_hispanic_over_60',
       'p_native_over_60', 'p_asian_over_60', 'p_disabled', 'geometry',
       'county', 'area'], dtype=object)

gdf.n_total_pop_sample

0      NaN
1      NaN
2      NaN
3      NaN
4      NaN
        ..
4575   NaN
4576   NaN
4577   NaN
4578   NaN
4579   NaN
Name: n_total_pop_sample, Length: 4580, dtype: float64

Geopandas

• GeoSeries
• GeoDataFrame
• County variable
• area/crs/project
• population density ## Studio
• Report number of tracts by county
• Median population tract population density by county
• Population density by county
  • two group bys and division
  • later show them dissove